Dynamic sensor operation and data processing based on motion information

ABSTRACT

Methods and apparatuses are disclosed for determining a characteristic of a device&#39;s object detection sensor oriented in a first direction. An example device may include one or more processors. The device may further include a memory coupled to the one or more processors, the memory including one or more instructions that when executed by the one or more processors cause the device to determine a direction of travel for the device, compare the direction of travel to the first direction to determine a magnitude of difference, and determine a characteristic of the object detection sensor based on the magnitude of difference.

TECHNICAL FIELD

The present disclosure relates generally to operation of sensors andprocessing of sensor captured data, and specifically to dynamicallydetermining sensors and processing of sensor captured data based onmotion information.

BACKGROUND

Various devices are equipped with object detection and tracking sensors(such as cameras, RADAR, SONAR, laser sensors/scanners, and so on) thatare capable of capturing information about a sensor's environment in oneor more directions. While some object detection sensors mayomni-directionally perceive their environment (such as some RADARsystems or LIDAR systems with rotating antennas), many object detectionsensors capture information in a direction of capture and within a fieldof capture based on the orientation of the sensor. Some object detectionsensors may be used for obstacle detection for a device. For example,some automobiles are equipped with SONAR sensors in the rear bumper sothat the automobile may notify the driver if an object is in closeproximity while reversing.

Object detection sensors require computing and power resources tocapture data. Additionally, a device requires computing and powerresources to process the captured data and to control the objectdetection sensors. As many devices (such as automobiles, airplanes,unmanned aerial vehicles, smartphones, and so on) incorporate objectdetection sensors, processing resources and power consumption associatedwith object detection sensors continues to increase.

SUMMARY

This Summary is provided to introduce in a simplified form a selectionof concepts that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tolimit the scope of the claimed subject matter.

Aspects of the present disclosure are directed to methods andapparatuses for determining a characteristic of a device's objectdetection sensor during use of the sensor. In one example, a device mayinclude an object detection sensor oriented in a first direction. Thedevice may also include one or more processors. The device may furtherinclude a memory coupled to the one or more processors, the memoryincluding one or more instructions that, when executed by the one ormore processors, cause the device to: determine a direction of travelfor the device, compare the direction of travel to the first directionto determine a magnitude of difference, and determine a characteristicof the object detection sensor based on the magnitude of difference.

In another example, a method for determining a characteristic of adevice's object detection sensor oriented in a first direction mayinclude determining a direction of travel for the device. The method mayfurther include comparing the direction of travel to the first directionto determine a magnitude of difference. The method may also includedetermining a characteristic of the object detection sensor based on themagnitude of difference.

In a further example, a non-transitory computer-readable storage mediumfor determining a characteristic of a device's object detection sensororiented in a first direction is disclosed. The storage medium may storeone or more programs containing instructions that, when executed by oneor more processors of a device, cause the device to perform operationsincluding determining a direction of travel for the device. Operationsmay further include comparing the direction of travel to the firstdirection to determine a magnitude of difference. Operations may alsoinclude determining a characteristic of the object detection sensorbased on the magnitude of difference.

In another example, a device for determining a characteristic of anobject detection sensor oriented in a first direction may include meansfor using the object detection sensor during operation of the device.The device may also include means for determining a direction of travelof the device. The device may further include means for comparing thedirection of travel to the first direction to determine a magnitude ofdifference. The device may also include means for determining acharacteristic of the object detection sensor based on the magnitude ofdifference.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure herein is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements.

FIG. 1A is an illustration depicting a side view of a device includingmultiple object detection sensors, in accordance with some aspects ofthe present disclosure.

FIG. 1B is an illustration depicting a bottom up view of the deviceincluding the multiple object detection sensors shown in FIG. 1A.

FIG. 1C is an illustration depicting a top down view of another deviceincluding multiple object detection sensors, in accordance with someaspects of the present disclosure.

FIG. 1D is an illustration depicting a side view of the device includingthe multiple object detection sensors shown in FIG. 1C.

FIG. 1E is an illustration depicting another device including multipleobject detection sensors, in accordance with some aspects of the presentdisclosure.

FIG. 2A is an illustration depicting multiple object detection sensorson a generic device oriented along one plane of capture.

FIG. 2B is an illustration depicting example fields of capture anddirections of capture for the object detection sensors shown in FIG. 2A.

FIG. 2C is an illustration depicting additional object detection sensorson the generic device shown in FIG. 2A oriented along the plane ofcapture.

FIG. 2D is an illustration depicting example fields of capture anddirections of capture for the object detection sensors shown in FIG. 2C.

FIG. 2E is an illustration depicting additional object detection sensorson the generic device shown in FIG. 2A oriented along an additionalplane of capture.

FIG. 2F is an illustration depicting additional object detection sensorson the generic device shown in FIG. 2E along both planes of capture.

FIG. 2G is an illustration depicting additional object detection sensorson the generic device shown in FIG. 2F along further planes of capture.

FIG. 3 is a block diagram of an example device, in accordance with someaspects of the present disclosure.

FIG. 4 is an illustration depicting a device with multiple possibledirections of travel in one plane of motion and including an objectdetection sensor, in accordance with some aspects of the presentdisclosure.

FIG. 5 is an example graph depicting an example change in an objectdetection sensor's capture rate, in accordance with some aspects of thepresent disclosure.

FIG. 6 is an example graph depicting another example change in an objectdetection sensor's capture rate, in accordance with some aspects of thepresent disclosure.

FIG. 7 is an illustration depicting a device with multiple objectdetection sensors during operation, in accordance with some aspects ofthe present disclosure.

FIG. 8 is an illustrative flow chart depicting an example operation fordetermining a characteristic of a device's object detection sensor, inaccordance with some aspects of the present disclosure.

FIG. 9 is an illustrative flow chart depicting another example operationfor determining a characteristic of a device's object detection sensor,in accordance with some aspects of the present disclosure.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forthsuch as examples of specific components, circuits, and processes toprovide a thorough understanding of the present disclosure. The term“coupled” as used herein means connected directly to or connectedthrough one or more intervening components or circuits. Also, in thefollowing description and for purposes of explanation, specificnomenclature is set forth to provide a thorough understanding of thepresent disclosure. However, it will be apparent to one skilled in theart that these specific details may not be required to practice theteachings disclosed herein. In other instances, well-known circuits anddevices are shown in block diagram form to avoid obscuring teachings ofthe present disclosure. Some portions of the detailed descriptions whichfollow are presented in terms of procedures, logic blocks, processingand other symbolic representations of operations on data bits within acomputer memory. These descriptions and representations are the meansused by those skilled in the data processing arts to most effectivelyconvey the substance of their work to others skilled in the art. In thepresent disclosure, a procedure, logic block, process, or the like, isconceived to be a self-consistent sequence of steps or instructionsleading to a desired result. The steps are those requiring physicalmanipulations of physical quantities. Usually, although not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated in a computer system.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the followingdiscussions, it is appreciated that throughout the present application,discussions utilizing the terms such as “accessing,” “receiving,”“sending,” “using,” “selecting,” “determining,” “normalizing,”“multiplying,” “averaging,” “monitoring,” “comparing,” “applying,”“updating,” “measuring,” “deriving” or the like, refer to the actionsand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

In the figures, a single block may be described as performing a functionor functions; however, in actual practice, the function or functionsperformed by that block may be performed in a single component or acrossmultiple components, and/or may be performed using hardware, usingsoftware, or using a combination of hardware and software. To clearlyillustrate this interchangeability of hardware and software, variousillustrative components, blocks, modules, circuits, and steps aredescribed below generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present disclosure. Also, the example devices may includecomponents other than those shown, including well-known components suchas a processor, memory and the like.

Example object detection sensors may include monocular cameras,stereoscopic cameras, infrared cameras, time of flight cameras, rangefinders, SONAR sensors, RADAR sensors, and laser sensors/scanners. Suchobject detection sensors may be used to determine a distance between thesensor and an object within the sensor's field of capture. For example,a stereoscopic camera captures multiple images at the same time fromdifferent vantages. The captured images from different vantages may becompared to determine a “depth” of an object captured in the multipleimages (i.e., an indication of the distance between the camera and theobject).

For example, unmanned aerial vehicles (such as aerial drones andquadcopters) may use object detection sensors for obstacle detection andavoidance. FIG. 1A is an illustration 100A depicting a side view of anexample unmanned aerial vehicle (UAV) 102 including a plurality ofobject detection sensors 104. FIG. 1B is an illustration 100B depictinga bottom up view of the example unmanned aerial vehicle 102 includingthe plurality of object detection sensors 104. In the exampleconfiguration of object detection sensors 104, the unmanned aerialvehicle 102 may use the object detection sensors 104 to detect obstacleswhile traveling along a plane parallel with the earth's horizon.Additional object detection sensors may also exist to detect obstaclesabove and/or below the unmanned aerial vehicle 102.

In another example, automobiles may be equipped with object detectionsensors (such as SONAR sensors and/or backup cameras) to detectobstacles while reversing. An automobile may be further equipped withobject detection sensors around the body of the automobile (includingthe rear) for obstacle detection in directions other than reverse. FIG.1C is an illustration 100C depicting a top down view of an exampleautomobile 106 including a plurality of object detection sensors 104.FIG. 1D is an illustration 100D depicting a rear view of the exampleautomobile 106. In the example configuration of object detection sensors104 for an automobile 106, the object detection sensors 104 may be usedto, for example, alert a driver or occupant of the automobile 106 of anobstacle, override user operation to avoid an obstacle (such as brakingin emergency situations, lane deviation adjustment, and so on), andassist in operating an automobile 106 for self-driving.

In a further example, virtual reality headsets or virtual realitydevices (such as a smartphone used within a virtual reality headset),where a user is removed or inhibited from sensing the physical world(such as sight as a result of wearing a VR headset and hearing as aresult of wearing headphones), may include object detection sensors toidentify a potential obstacle in order to assist a user in avoidingobstacles while moving. FIG. 1E is an illustration 100E depicting anexample virtual reality headset 108 including a plurality of objectdetection sensors 104. An example orientation of the object detectionsensors 104 may include one or more object detection sensors 104 locatedon a front of the headset 108, one or more object detection sensors 104located on a left side of the headset 108, and one or more objectdetection sensors 104 located on a right side of the headset 108. In theexample configuration of object detection sensors 104 for the virtualreality headset 108, the object detection sensors 104 may be used toidentify an obstacle. Once an obstacle is identified, the user isnotified of the approaching obstacle by rendering an obstacle in theuser's field of capture within the virtual reality experience and/orproviding an audible notification for the obstacle.

While a plurality of example devices that may include or be coupled toone or more object detection sensors 104 are illustrated in FIG. 1A-FIG.1E, the present disclosure is not limited to the illustrated examples ora specific device, and aspects of the present disclosure may relate toany periodically moving or moveable device (such as boats, aircraft,tractors, wireless communication devices, headphones, and so on).

In some aspects, the object detection sensors 104 are positioned todetect obstacles in an axis of travel or plane of travel of the device.For example, a car does not change height in relation to the ground anddoes not roll, pitch, or yaw in relation to the ground. Thus, the carhas one plane of travel approximately parallel to the ground. Objectdetection sensors 104 may be positioned on the car to detect obstaclesin the one plane of travel. Orienting the object detection sensors 104to detect obstacles in a plane of travel may comprise positioning ororienting the object detection sensors 104 so that the direction ofcapture for each object detection sensor 104 aligns approximately withthe plane of travel. As a result, the plane of travel may also be a“plane of capture” in which objects are detected.

FIG. 2A is an illustration 200A depicting an example orientation ofmultiple object detection sensors 104 on a generic device 202 along oneplane of capture 204. While the object detection sensors 104 areillustrated as oriented along the plane of capture 204, the objectdetection sensors 104 may otherwise be positioned as long as each objectdetection sensor's field of capture covers a portion of the plane ofcapture 204. FIG. 2B is an illustration 200B depicting an example fieldof capture 206 and an example direction of capture 208 of each objectdetection sensor 104 of the generic device 202 shown in FIG. 2A. Ifdevice 202 is a car, then FIG. 2A illustrates a side view of the carwith object detection sensors 104 positioned at the front, back and bothsides of the car. Continuing with the example of a car, FIG. 2Billustrates a top down view of the car shown in FIG. 2A and alsoillustrates the field of capture 206 and direction of capture 208 forthe object detection sensor 104 on each side of the car.

While one object detection sensor 104 per side is illustrated in theexamples in FIG. 2A and FIG. 2B, any number of object detection sensors104 may be positioned any distance from one another. FIG. 2C is anillustration 200C depicting the generic device 202 shown in FIG. 2Aincluding additional object detection sensors 104 oriented along the oneplane of capture 204. FIG. 2D is an illustration 200D depicting anexample field of capture 206 and example direction of capture 208 ofeach object detection sensor 104 of the generic device 202 shown in FIG.2C. Again continuing with the example of a car, object detection sensorsmay be positioned along the corners of a bumper in order to observe moreof the plane of travel for the car (such as if an object is in closeproximity while the car is turning into a parking space).

Many devices have more than one plane of travel. For example, a crane atshipping ports (which places containers onto flatbeds of shippingtrucks) may move in any direction horizontal to the ground as well as upand down in order to lift and lower containers. Therefore, the cranemoves within a three dimensional space that may be defined by two planesof travel: a plane parallel to the ground (similar to a car) and a planeperpendicular to the ground (the plane defining lifting and lowering thecontainers). FIG. 2E is an illustration 200E depicting an exampleorientation on the generic device 202 shown in FIG. 2A of multipleobject detection sensors 104 along two planes of capture 204. FIG. 2F isan illustration 200F depicting the generic device 202 shown in FIG. 2Ewith additional object detection sensors 104 along the two planes ofcapture 204. Some devices (such as aerial drones and other aircraft), inaddition to moving in a three dimensional space, also may roll, pitch,and/or yaw when moving. FIG. 2G is an illustration 200G depicting anexample orientation on the generic device 202 shown in FIG. 2F withadditional object detection sensors 104 along additional planes ofcapture 204.

While some example orientations of object detection sensors areillustrated, object detection sensors may be positioned in any mannersuitable for detecting obstacles to a device's movement. Furthermore,while direction of capture is illustrated as static relative to anobject detection sensor, some sensors may have a variable direction ofcapture. For example, some laser scanners use mirrors to change thedirection of the laser emitting from the scanner (such as bar codescanners). Additionally, while geometric planes are used in describingcapturing information by object detection sensors and movement bydevices, some devices may move freely in a three dimensional space andsome sensors may capture information outside of specific planes. Forexample, airplanes and quadcopters may move freely in three dimensionalspace, some cameras may have a wide field of capture, and some laserscanners may have a variable direction of capture. Therefore, thepresent disclosure should not be limited to the provided examples.

As many devices incorporate object detection sensors and the amount ofinformation captured by object detection sensors increases, conservingprocessing resources and power consumption while still operating anobject detection sensor may be needed. A device may determine (such asadjusting) various aspects of an object detection sensor to reduce itscomputing resources or power consumption. For a SONAR sensor, the devicemay determine a speaker transmit power, which impacts the soundingrange. The device may additionally or alternatively determine thesounding frequency. For a RADAR sensor, the device may determine anantenna power, which impacts the ability to receive wireless signals.For a laser sensor, the device may determine a transmit power for thelaser emitter, may determine the frequency of the laser, may determinethe movement of the laser by the scanner, and/or may determine thestrobe frequency of the emitter. For a LIDAR sensor, the device maydetermine the frequency of the laser and/or may determine the transmitpower for the laser emitter. For a camera (such as a monocular camera, astereoscopic camera, an infrared camera, a time of flight camera, arange finder, and so on), the device may determine the frame capturerate (frame rate), the image resolution, and the color depth or palette(if capturing visible light). For a stereoscopic camera (which mayinclude multiple monocular cameras), the device may also determine thenumber of cameras to use in capturing frames (such as using one cameraor using two cameras). Additionally, if the multiple cameras for thestereoscopic camera are not equivalent, the device may determine whichcamera to use (if not to use both). For example, one camera may be usedin lieu of the other camera based on better resolution, better low lightimage capture, less power consumption, and so on.

In addition or alternative to controlling aspects of the objectdetection sensor, the device may determine how to process theinformation captured by an object detection sensor. For example, thedevice may sometimes discard some captures (such as every other capture)to conserve computing resources. In another example, the device may onlypartially process captured information. Thus, the device may determinethe frequency for processing information captured by an object detectionsensor and/or determine which portion of the captured information to beprocessed by the device.

Information captured by an object detection sensor is of more relevancein obstacle detection at certain times than others. For example, whenthe device is a car, information from a backup camera is more relevantin obstacle detection for the car while reversing than while drivingforward or in park. In another example, when the device is a virtualreality headset, information captured by an object detection sensor onthe front of a virtual reality headset is more relevant in obstacledetection for the user while walking forward than while sitting or beingstationary. In yet another example of a drone quadcopter, informationcaptured by a forward facing object detection sensor is more relevant inobstacle detection for the drone when moving forward than while movingup or down, pitching, yawing, or rolling. In addition, informationcaptured by a forward facing object detection sensor may be leastrelevant when moving backwards.

A device may determine its motion using one or more motion sensors,including one or more accelerometers, one or more gyroscopes, one ormore compasses, one or more pressure sensors, and one or more globalpositioning systems (GPS). Device motion information may be used todetermine or adjust capture aspects of the object detection sensorand/or determine or adjust how to process captured information from anobject detection sensor. For example, if a device is moving in adirection similar to a direction of capture for a camera on the device(such as while a car is backing up and using a backup camera), thecamera may be adjusted to have a higher frame capture rate (such as 30frames per second) than if the device is moving in a different direction(such as 5 frames per second when the car is driving forward or inpark). In another example, the image capture resolution may be increasedif the device is moving in the direction similar to the direction ofcapture for the camera and decreased if the device is moving in adifferent direction than the direction of capture.

Co-pending U.S. patent application Ser. No. 15/224,904, titled “Systemand Method of Dynamically Controlling Parameters for Processing SensorOutput Data for Collision Avoidance and Path Planning”, filed Aug. 1,2016 and issued as U.S. Pat. No. 10,126,722 on Nov. 13, 2018, describesadjusting a sampling rate of a sensor on a vehicle based on thedirection of travel of the vehicle. This co-pending application ishereby incorporated by reference in its entirety.

In addition to determining a characteristic of an object detectionsensor based on a direction of travel of a device, determiningcharacteristics of an object detection sensor also may be based on amagnitude of difference (such as a degree of difference) between thedirection of travel and the direction of capture for the objectdetection sensor. Determinations further may be based on one or morepredictions about future motion of the device. For example, if a carsenses it is decelerating while driving forward, the car may increasethe frame rate of the backup camera in predicting that the reversedirection may become more relevant to obstacle avoidance. In one aspectof predicting future motion, the device may determine its trajectory. Ifthe device determines its trajectory will bring the device's futuredirection of travel closer to the object detection sensor's direction ofcapture, the device may use a predicted future direction of travel orthe trajectory to determine the characteristic for the one or more ofthe object detection sensors.

In some aspects of the present disclosure, the device may determine adensity or number of objects in the device's environment, which may beused to determine the characteristic of the object detection sensor. Ifthe density of objects increases, there may be a higher likelihood of anobject being an obstacle to the device's motion. Therefore, the devicemay mitigate an adjustment to an object detection sensor even if thedirection of capture does not substantially align with the device'sdirection of travel. For example, where the device is a self-drivingcar, if there are other automobiles surrounding the car on the road, thedevice may determine to reduce a frame rate of a rear camera by asmaller magnitude than if the car would be alone on the road.

In some example implementations, one or more object detection sensors ofthe device are used to determine the density of objects in the device'senvironment. Thus, an object detection sensor's adjustment may bemitigated based on captures from one or more other object detectionsensors as well as its own captures. In other example implementations,mitigation of adjustments to an object detection sensor may be basedsolely on information captured by that object detection sensor.

In some further aspects of the present disclosure, the characteristic ofthe object detection sensor may be based on whether the device hasidentified an object in the device's environment as a potentialobstacle. In determining if an object is a potential obstacle, thedevice may use information captured from one or more object detectionsensors to determine the potential for a future collision between theobject and the device. For example, the device, in using captures fromone or more object detection sensors, may determine that an object ismoving in relation to its environment and the device. The device maydetermine one or more of the object's direction of travel relative tothe device, the object's speed of travel relative to the device, and theobject's trajectory relative to the device. In some exampleimplementations, the object's direction of travel and speed can becompared to the device's direction of travel and speed to determine aprobability for collision. Additionally or alternatively, the device maycompare the object's trajectory to the device's trajectory to determinea probability for collision. In determining a probability for collision,the device may use a margin of error in comparing trajectories and/ordirections of travel and speeds. The margin of error may account forpossible changes in acceleration of the object or the device, possiblechanges in direction or trajectory of the object or the device, and/orestimation errors from determining an object's movement.

In an example of identifying an object as a potential obstacle, if thedevice is a quadcopter drone and another aerial vehicle is in thedrone's environment during flight, a stereoscopic camera may capture animage of the vehicle and use successive captures to determine adirection of travel and speed of the vehicle relative to the drone. Thedrone may then compare the vehicle's direction of travel and speed tothe drone's direction of travel and speed to determine a possibility ofcollision between the vehicle and drone. In comparing directions oftravel and speeds, the device may use a margin of error to account forthe vehicle and/or the drone potentially changing trajectories by, e.g.,a defined degree of freedom (such as up to 10 degrees of difference fromthe current direction of travel, 10 percent difference in speed of thedevice and object, and so on).

If the device identifies an object as a potential obstacle, the devicemay enable the one or more object detection sensors capturinginformation about the object to operate in an increased manner. As aresult, the device may have better fidelity in tracking the object andoptionally taking any actions for avoiding the potential obstacle. Forexample, if a camera's frame rate is to be reduced to 5 frames persecond from 30 frames per second as a result of comparing the device'sdirection of travel to the camera's direction of capture, but the cameracaptures an object that the device identifies as a potential obstacle,the device may determine to keep the camera's frame rate at 30 framesper second while tracking the object. Additional processes and examplesof operating an object detection sensor are described below.

FIG. 3 is a block diagram of an example device 300 (such as one of thedevices shown in FIGS. 1A-E) that may be used to perform aspects of thepresent disclosure. The device 300 may be any suitable device capable ofcapturing information using an object detection sensor, including, forexample, wireless communication devices (such as camera phones,smartphones, tablets, dash cameras, laptop computers, desktop computers,and so on), automobiles, construction equipment, aircraft (such asairplanes, helicopters, drones, and so on), and seacraft (such as ships,submarines, buoys, and so on). The device 300 is shown in FIG. 3 toinclude a processor 302, a memory 304 storing instructions 306, awireless interface 308 coupled to antennas ANT1-ANTn, one or more objectdetection sensors 314 (such as a camera 316, a SONAR speaker 320 andSONAR microphone 322, a laser scanner 326, and/or a RADAR antenna 330),a camera controller 318 coupled to the camera 316, a SONAR controller324 coupled to the SONAR speaker 320 and the SONAR microphone 322, aLaser Sensor Controller 328 coupled to the laser scanner 326, one ormore motion sensors 332 (such as an accelerometer 334, a gyroscope 336,a compass 338, a pressure sensor 340, and a GPS antenna 342), and one ormore motion sensor controllers 344 coupled to the one or more motionsensors 332. The device 300 may also include a power supply 346, whichmay be coupled to or integrated into the device 300.

The processor 302 may be any suitable one or more processors capable ofexecuting scripts or instructions of one or more software programs (suchas instructions 306) stored within memory 304. In some aspects of thepresent disclosure, the processor 302 may be one or more general purposeprocessors that execute instructions 306 to cause the device 300 toperform any number of different functions or operations. In additionalor alternative aspects, the processor 302 may include integratedcircuits or other hardware to perform functions or operations withoutthe use of software. While shown to be coupled to each other via theprocessor 302 in the example of FIG. 3, the processor 302, memory 304,object detection sensors 314, camera controller 318, SONAR controller324, laser sensor controller 328, wireless interface 308, and one ormore motion sensor controllers 344 may be coupled to one another invarious arrangements. For example, the processor 302, memory 304, objectdetection sensors 314, camera controller 318, SONAR controller 324,laser sensor controller 328, wireless interface 308, and one or moremotion sensor controllers 344 may be coupled to each other via one ormore local buses (not shown for simplicity).

The camera controller 318 may include one or more image signalprocessors to process captured image frames or video provided by thecamera 316. A SONAR controller 324 and laser sensor controller 328 mayalso include one or more processors to process the information capturedby the SONAR microphone 322 and the laser scanner 326, respectively.

The wireless interface 308 may include at least a number of transceivers310 and a baseband processor 312. The transceivers 310 may be coupled toantennas ANT1-ANTn. In some aspects, the device 300 may include anantenna selection circuit (not shown for simplicity) that canselectively couple the transceivers 310 to different antennas ANT1-ANTn.The transceivers 310 may be used to transmit signals to and receivesignals from other devices including, for example, an access point, abase station, other wireless communication devices, and so on. Althoughnot shown in FIG. 3 for simplicity, the transceivers 310 may include anynumber of transmit chains to process and transmit signals to otherdevices via antennas ANT1-ANTn, and may include any number of receivechains to process signals received from antennas ANT1-ANTn.

The baseband processor 312 may be used to process signals received fromthe processor 302 and the memory 304 and to forward the processedsignals to transceivers 310 for transmission via one or more of antennasANT1-ANTn, and may be used to process signals received from one or moreof antennas ANT1-ANTn via transceivers 310 and to forward the processedsignals to the processor 302 and the memory 304. More specifically, thebaseband processor 312, which may be any suitable well-known basebandprocessor, encodes signals for transmission from the device 300 via thetransceivers 310, and decodes signals received from other wirelessdevices via the transceivers 310. The transmit chains within thetransceivers 310 may include mixers to up-convert signals from abaseband frequency to a carrier frequency for transmission from device300, and the receive chains within the transceivers 310 may includemixers to down-convert received signals from the carrier frequency tothe baseband frequency.

The one or more object detection sensors 314 of device 300 may be anynumber of one or more cameras 316, one or more SONAR sensors (includinga SONAR speaker 320 and a SONAR microphone 322), one or more laserscanners 326, and one or more RADAR sensors (including one or more RADARantennas 330 coupled to the wireless interface 308). While device 300 isillustrated as including at least one of each type of object detectionsensor 314, the device 300 may include a subset of the illustratedobject detection sensors 314 and their respective controllers. Regardingthe RADAR sensor, the wireless interface 308 may process the wirelesssignals received by RADAR antenna 330. In other example implementations,the wireless interface 308 may use one or more of ANT1-ANTn to receivethe wireless signals for RADAR. Device 300 may also include other objectdetection sensors not shown in FIG. 3. For example, device 300 mayinclude one or more time of flight cameras, one or more range finders,and/or one or more LIDAR systems. Thus, the present disclosure shouldnot be limited to the provided examples.

The one or more motion sensors 332 may include one or more from anaccelerometer 334, a gyroscope 336, a compass 338, an atmosphericpressure sensor 340, and a Global Positioning System (including one ormore GPS antennas 342 attached to the one or more motion sensorcontrollers 344). While device 300 is illustrated as including at leastone of each type of motion sensor 332, the device 300 may include asubset of the illustrated motion sensors 332.

In some example implementations, processor 302 (in executinginstructions 306 stored in memory 304) may compare the device's motion(determined using the one or more motion sensors 332 and motion sensorcontroller 344) with the direction of capture for an object detectionsensor 314 (such as a direction of capture for camera 316), determine anadjustment to one or more characteristics of the object detection sensor314 (such as the frame rate for camera 316) based on the comparison, andsend instructions to the object detection sensor's respective controller(such as camera controller 318 for camera 316) to adjust the one or morecharacteristics of the sensor 314 during operation.

In some other example implementations, the one or more processors 302may use the comparison to adjust how to process information captured bythe one or more object detection sensors. For example, the one or moreprocessors 302 may determine to reduce processing of sensor captures(such as skipping captures to process, processing only a portion of thecaptures, and so on) by a magnitude corresponding to a differencebetween the device's direction of travel relative to the objectdetection sensor's direction of capture.

The below examples in the disclosure describe adjusting a characteristicof an object detection sensor. However, some aspects of the presentdisclosure include adjusting processing the captured information from anobject detection sensor in addition to or alternative to adjusting acharacteristic of the object detection sensor. In describing adjusting acharacteristic of an object detection sensor, many of the below examplesdescribe adjusting a frame capture rate for a camera. However, someaspects of the present disclosure include types of sensors other than acamera and characteristics of the sensor for adjustment other than theframe capture rate. Example characteristics that may be adjusted includea power of a RADAR antenna, a transmit power of a speaker of a SONARsensor, a power of an emitter of a laser sensor or laser scanner, aresolution of a camera, a color depth of a camera, and a number ofobject detection sensors to be used (for multiple object detectionsensors with overlapping directions of capture). Thus, the providedexamples are for illustrative purposes, and the present disclosureshould not be limited to the provided examples.

FIG. 4 is an illustration 400 depicting an example device 202 withmultiple possible directions of travel 402A-402H in one plane of motion,wherein the device 202 includes an object detection sensor 104 with afield of capture 404 and a direction of capture 406 (as projected ontothe one plane of motion). In the example, the object detection sensor104 is a stereoscopic camera, but the sensor 104 may be of any type(such as any object detection sensor 314 described above). For thecamera, the variable capture rate is signified by arrow 408 (such as 5fps, 15 fps, 30 fps, and so on).

In some aspects of the present disclosure, the device's direction oftravel may be used to determine the capture rate 408 for sensor 104. Inone example implementation, the capture rate 408 is dependent on acomparison between the sensor's direction of capture 406 and thedevice's direction of travel (such as directions 402A-402H). Forexample, if the device's direction of travel is direction 402A (matchingthe direction of capture 406), then the capture rate 408 for the objectdetection sensor 104 may be set to a defined highest capture rate (suchas the maximum allowable capture rate by the object detection sensor104). In another example, if the device's direction of travel isdirection 402E (approximately opposing the direction of capture 406),then the capture rate 408 for the object detection sensor 104 may be setto a defined lowest capture rate (such as 5 fps). Thus, the objectdetection sensor 104 might still capture information when the device'sdirection of travel is direction 402E, but the amount of informationcollected is less than if the direction of travel is direction 402A. Thehighest capture rate and/or the lowest capture rate may be defined bythe manufacturer, defined by hardware or software limitations of thesensor and/or the device, and/or defined by the device's user.

If the device's direction of travel is between direction 402A anddirection 402E (such as directions 402B-402D or directions 402F-402H),then the capture rate 408 for the object detection sensor 104 may bebetween the defined lowest capture rate and the highest defined capturerate. In some aspects, the change in capture rate 408 from a lowestcapture rate to a highest capture rate may be linear. FIG. 5 is anexample graph 500 depicting an example change in capture rate based onthe direction of capture 406 as compared to the direction of travel. Thevertical axis indicates the capture rate 408 of the object detectionsensor 104. The horizontal axis indicates a magnitude of differencebetween the direction of capture 406 and the direction of travel (suchas directions 402A-H). For one plane of travel, the largest differencebetween the direction of capture 406 and the direction of travel 402E is180 degrees.

Graph 500 indicates that the capture rate is linearly dependent on themagnitude of difference. Point 502, at zero degrees difference betweenthe direction of travel and the direction of capture (such as directions402E and 406), illustrates that the object detection sensor capture rateis at its highest defined capture rate. Point 504, at 45 degreesdifference (such as directions 402B or 402H and direction 406), showsthat the capture rate may be lower than the highest capture rate by afourth of the difference between the lowest capture rate and the highestcapture rate. Point 506, at 90 degrees difference (such as directions402C or 402G and direction 406), shows that the capture rate may behalfway between the lowest capture rate and the highest capture rate.Point 508, at 135 degrees difference (such as directions 402D or 402Fand direction 406), shows that the capture rate may be higher than thelowest capture rate by one fourth of the difference between the lowestcapture rate and the highest capture rate. In an illustrative example,if the lowest capture rate 408 is 5 fps and the highest capture rate is45 fps, then points 502-510 would indicate capture rates of 45, 35, 25,15, and 5 fps, respectively.

In other aspects of the present disclosure, the capture rate and themagnitude of difference between the direction of travel and thedirection of capture may be related in a non-linear fashion. Forexample, referring back to FIG. 4, the field of capture covers anarea/volume of directions wider than the direction of capture. Hence,the object detection sensor 104 of a device 202 with a direction oftravel between direction 402B and direction 402H proximal to thedirection of capture 406 (such as direction of travel 402A) may be morehelpful in determining obstacles than for directions of travel distal tothe direction of capture 406. In general, object detection sensor 104may become more useful in detecting obstacles as the magnitude ofdifference between direction of capture and direction of traveldecreases. Therefore, for degrees of difference, the decrease in capturerate may be lower for a change in degree of difference between, e.g., 0and 45 degrees than for a change in degree of difference between, e.g.,45 degrees and 90 degrees. In some alternative example implementations,a magnitude of difference related to a change in capture rate may berepresented by a polynomial function (such as a first order polynomialfunction) or a power function.

FIG. 6 is an example graph 600 depicting another example change incapture rate based on the direction of capture 406 as compared to thedirection of travel (such as 402A-402H). As illustrated, the capturerate vs. the magnitude of difference between the direction of captureand the direction of travel is a non-linear relationship (such asdefined by a first order function). For graph 600, the difference inobject detection sensor capture rates between 0 degrees (point 602) and45 degrees (point 604) may be less than the difference between 45degrees (point 604) and 90 degrees (point 606), which may be less thanthe difference between 90 degrees (point 606) and 135 degrees (point608), which further may be less than the difference between 135 degrees(point 608) and 180 degrees (point 610).

In some example implementations, the width of the field of capture 404may affect the rate of change for the object detection sensor capturerate 408. As a result, the slope and/or curvature in graph 600 may varydepending on the width of the object detection sensor's field ofcapture. For example, the initial rate of change from the highestcapture rate may be greater for a narrower field of capture than for awider field of capture. In another example rate of change for the sensorcapture rate, the object detection sensor's capture rate may stay at afixed capture rate for a span of directions of travel. For example, fordirections of travel between direction 402B and direction 402H anddirection of capture 406 (indicating a degree of difference to be lessthan or equal to 45 degrees), the object detection sensor may captureframes at the highest capture rate. When a degree of difference exceeds45 degrees, the device may then reduce the capture rate dependent on thedegree of difference. While some examples of relationships between thecapture rate and the difference between the direction of capture and thedirection of travel have been provided, any correlation between thesensor capture rate and the magnitude of difference may exist, andembodiments of the disclosure should not be limited to the aboveillustrative examples.

In some aspects of the present disclosure, the capture rate may also bedependent on the trajectory of the device. For example, the device,using one or more motion sensors, may determine its trajectory orcurrent path traveled. The device may use such information to predict afuture direction of travel. The device may thus use the future predicteddirection of travel to adjust the object detection capture rate. Forexample, the device may compare the predicted direction of travel to thedirection of capture to determine a magnitude of difference and use thedetermined magnitude of difference to determine a magnitude ofadjustment for the object detection capture rate.

In some additional aspects of the present disclosure, the capture ratemay be dependent on the speed or velocity of the device. For example, aslow moving device may have more need for an object detection sensor todetect obstacles in an opposite direction of movement than a fast movingdevice. One reason may be that moving obstacles (such as other cars,other drones, and so on) may have a better chance to contact a slowmoving device from the device's distal end of movement than a fastermoving device. Thus, the object detection sensor may have a highercapture rate for a device at a slower velocity than for a device at afaster velocity. In determining the speed, the device may determine avector or a magnitude. The vector may also be used to determine orpredict if the future direction of travel is to change from the currentdirection of travel.

In additional aspects of the present disclosure, the capture rate may bedependent on an acceleration of the device. In some exampleimplementations, an acceleration may indicate a predicted futurevelocity that is different than the current velocity. If the sensorcapture rate also is dependent on the velocity of the device, the devicemay use the acceleration to predict and preemptively adjust the capturerate by a magnitude based on the predicted future velocity. For example,if a car is moving at 50 miles per hour and begins to decelerate, thefuture velocity may be predicted to be, e.g., 35 miles per hour. The carmay therefore increase the capture rate of the object detection sensorbased on predicting that the velocity is to decrease.

In some other example implementations of using acceleration, anacceleration vector may be used to determine if the direction of travelis to change. For example, if a device is traveling in one direction(such as direction of travel 402E in FIG. 4) and the acceleration vectoris 90 degrees to the direction of travel (such as along direction 402C),a predicted future direction of travel may be determined to be somewherebetween direction 402C and direction 402E. Thus, the device maypreemptively adjust the object detection sensor capture rate dependenton the predicted direction of travel.

In addition or alternative to an object detection sensor's capture ratebeing dependent on one or more of the device's trajectory, speed, andacceleration, the capture rate may be dependent on a density of objectsin the device's environment. A device in an environment with potentialobstacles, especially moving objects (such as cars, drones, and so on)may have a need to detect such potential obstacles to determine if thereis a possibility of collision and for obstacle avoidance. When moreobjects exist in the device's environment, there may be a greater chanceof a collision between the device and one of the objects.

FIG. 7 is an illustration 700 depicting an example device 202 withmultiple object detection sensors 702A, 702B, and 702C with respectivefields of capture 704A, 704B, and 704C and capturing a density ofobjects 706 in the device's environment. The device 202 may determinethe number of objects, and if the number of objects is greater than athreshold, the device 202 may adjust the capture rate for one or more ofthe object detection sensors 702A-702C. In some example implementations,each object detection sensor's capture rate is based on the number ofobjects 706 captured by that object detection sensor. In some otherexample implementations, each object detection sensor's capture rate maybe based on the number of objects 706 captured by multiple objectdetection sensors. The threshold for the number of objects may be basedon one or more of the speed of device 202, the current capture rate ofone or more object detection sensors 702A-702C, the direction of travel,a user defined threshold value, a predefined static value, and so on.Additionally or alternatively, the device may compare the number ofobjects captured by each object detection sensor in order to adjust thecapture rate of the object detection sensors relative to one another.For example, one object is detected in the field of capture 704A forobject detection sensor 702A while five objects are detected in thefield of capture 704C for object detection sensor 702C. Since the numberof objects captured by object detection sensor 702A is less than thenumber of objects captured by the object detection sensor 702C, thecapture rate for object detection sensor 702A may be determined to bereduced more than the capture rate for object detection sensor 702C(independent of the magnitude of difference between the direction oftravel and the direction of capture).

An object detection sensor's capture rate also may be dependent onwhether an object has been identified as a potential obstacle. Referringback to FIG. 7, the device 202 may have a direction of travel 708.Additionally, the device 202 may determine from captures by the objectdetection sensor 702A that object 710 has a predicted path of travel712. For example, the device 202 may compare sequential captures from astereoscopic camera (which provides a depth measurement) to determine apast movement of the object 710 in order to predict a path of travel712.

The device 202 may compare the device's direction of travel 708(optionally including the trajectory, speed and/or acceleration of thedevice 202) to the predicted path of travel 712 in order to determine ifthe object 710 may collide with the device 202. In determining apossible collision, the device 202 may use a margin of error for thedevice's direction of travel 708 and/or the device 202 may use a marginof error for the object's predicted path of travel 712. Thus, if acollision is predicted using a difference in the direction of travel 708and/or the predicted path of travel 712 within the margin of error, thedevice 202 may identify the object as a potential obstacle. Inidentifying an object 710 as a potential obstacle, the device 202 mayadjust the capture rate for the object detection sensor 702A. The device202 may also predict if a different object detection sensor may captureobject 710 before a potential collision. If another object detectionsensor may capture the object, the device 202 may also adjust thecapture rate for that object detection sensor.

In an example of adjusting the capture rate when identifying an objectas a potential obstacle and/or based on the density of objects in adevice's environment being above a threshold, the device 202 mayoverride a determined capture rate for the object detection sensor andset the capture rate to the highest capture rate for the sensor. In analternative example of adjusting the capture rate, the device 202 mayadjust the rate of change of the capture rate as compared to themagnitude of difference (such as illustrated in FIG. 5 and FIG. 6). Inyet another example, the device may use a vote checking system orsimilar to determine whether to adjust and the magnitude of adjustmentfor the capture rate of an object detection sensor.

While the above examples are described using one plane of travel andcapture, some example implementations apply for multiple planes oftravel (and capture). For example, vectors for a direction of travel anddirection of capture in a three-dimensional space (which may be definedby two or more planes of travel) may be compared in order to determine amagnitude of difference. Thus, the present disclosure should not belimited to one plane of travel or plane of capture.

FIG. 8 is an illustrative flow chart depicting an example operation 800for a device (such as device 202 in FIG. 2A-FIG. 4 and FIG. 7, device300 in FIG. 3, and so on) to determine a characteristic of an objectdetection sensor (such as adjusting the capture rate of camera 702A-702Cin FIG. 7, capture rate 408 in FIG. 4, and so on) oriented in a firstdirection to have a direction of capture (such as direction of capture406 in FIG. 4, direction 208 in FIG. 2B and FIG. 2D, and so on) duringuse of the object detection sensor. Beginning at 802, the device mayoptionally be operating, using an object detection sensor during suchoperation. For example, referring back to FIG. 4, a device 202 may usestereoscopic camera 104 with a direction of capture 406 and a field ofcapture 404 to capture frames to be used for identifying possibleobstacles to device 202.

Proceeding to 804, the device determines its direction of travel usingone or more motion sensors (such as motion sensors 332 in FIG. 3). Afterdetermining the device's direction of travel in 804, the device comparesthe direction of travel to the object detection sensor's direction ofcapture to determine a magnitude of difference (806). The device maythen use the magnitude of difference to determine a characteristic ofthe object detection sensor (808) by a magnitude based on the magnitudeof difference. For example, referring to FIG. 4, the device 202 mayadjust a capture rate 408 based on a determined magnitude of differencebetween the direction of capture 406 and the direction of travel fordevice 202. In other examples, the device 202 may determine a resolutionor color depth for a camera, a transmit power for a laser scanneremitter, a transmit power for a speaker of a SONAR sensor, or a powerfor a RADAR antenna. Referring back to FIG. 8, the device may optionallycontinue to use the object detection sensor with the determinedcharacteristic (810). In some aspects of the present disclosure, theprocess of determining (such as adjusting) a characteristic of theobject detection sensor may be repeated as necessary.

As previously described, the determination of a characteristic of theobject detection sensor (such as a capture rate of a camera) may bedependent on more than a determined magnitude of difference between thedevice's direction of travel and the object detection sensor's directionof capture. For example, the characteristic of the object detectionsensor further may be dependent on a trajectory of the device, a speedof the device, an acceleration of the device, a density of objects inthe device's environment, and/or whether a potential obstacle to thedevice is identified.

FIG. 9 is an illustrative flow chart depicting another example operation900 for a device to determine (such as adjusting) a characteristic of anobject detection sensor. Flow chart 900 includes operations similar tothose described in illustrative flow chart 800, and further includesadditional optional processes during operation of the device. Beginningat 902, the device may optionally be in operation, using an objectdetection sensor to capture information. Proceeding to 904, the devicemay determine its direction of travel using one or more motion sensors.The device may then compare the device's direction of travel to thedirection of capture for the object detection sensor in order todetermine a magnitude of difference (906). The device may alsooptionally determine one or more of: the device's trajectory (908); thedevice's speed or velocity (910); the device's acceleration (912); adensity of objects in the device's environment (914); and a potentialobstacle to the device (916).

Proceeding to 918, the device may thus use the determined magnitude ofdifference and the one or more determinations from 908-916 to determinea characteristic of the object detection sensor (such as a rate ofcapture, transmit power, resolution, and so on) by a magnitude dependenton the magnitude of difference and one or more determinations. Thedevice may then continue to use the object detection sensor with theadjusted characteristic (920).

The techniques described herein may be implemented in hardware,software, firmware, or any combination thereof, unless specificallydescribed as being implemented in a specific manner. Any featuresdescribed as modules or components may also be implemented together inan integrated logic device or separately as discrete but interoperablelogic devices. If implemented in software, the techniques may berealized at least in part by a non-transitory processor-readable storagemedium (such as memory 304 in FIG. 3) comprising instructions 306 that,when executed by processor 302, performs one or more of the methodsdescribed above. The non-transitory processor-readable data storagemedium may form part of a computer program product, which may includepackaging materials.

The non-transitory processor-readable storage medium may comprise randomaccess memory (RAM) such as synchronous dynamic random access memory(SDRAM), read only memory (ROM), non-volatile random access memory(NVRAM), electrically erasable programmable read-only memory (EEPROM),FLASH memory, other known storage media, and the like. The techniquesadditionally, or alternatively, may be realized at least in part by aprocessor-readable communication medium that carries or communicatescode in the form of instructions or data structures and that can beaccessed, read, and/or executed by a computer or other processor.

The various illustrative logical blocks, modules, circuits andinstructions described in connection with the embodiments disclosedherein may be executed by one or more processors, such as processor 302in FIG. 3. Such processor(s) may include but are not limited to one ormore digital signal processors (DSPs), general purpose microprocessors,application specific integrated circuits (ASICs), application specificinstruction set processors (ASIPs), field programmable gate arrays(FPGAs), or other equivalent integrated or discrete logic circuitry. Theterm “processor,” as used herein may refer to any of the foregoingstructures or any other structure suitable for implementation of thetechniques described herein. In addition, in some aspects, thefunctionality described herein may be provided within dedicated softwaremodules or hardware modules configured as described herein. Also, thetechniques could be fully implemented in one or more circuits or logicelements. A general purpose processor may be a microprocessor, but inthe alternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

While the present disclosure shows illustrative aspects, it should benoted that various changes and modifications could be made hereinwithout departing from the scope of the appended claims. Additionally,the functions, steps or actions of the method claims in accordance withaspects described herein need not be performed in any particular orderunless expressly stated otherwise. For example, 908-916 in FIG. 9, ifperformed by the device, may be performed in any order and at anyfrequency. Furthermore, although elements may be described or claimed inthe singular, the plural is contemplated unless limitation to thesingular is explicitly stated. For example, while one determination oradjustment and determining or adjusting one characteristic of an objectdetection sensor is described, a characteristic may be determined oradjusted multiple times and multiple characteristics may be determinedor adjusted by the device. Accordingly, the disclosure is not limited tothe illustrated examples and any means for performing the functionalitydescribed herein are included in aspects of the disclosure.

What is claimed is:
 1. A device, comprising: a camera including a firstdirection of capture and a first frame capture rate; one or moreprocessors; and a memory coupled to the one or more processors, thememory including one or more instructions that, when executed by the oneor more processors, cause the device to: determine a direction of travelfor the device; compare the direction of travel to the first directionof capture to determine a magnitude of difference; and adjust the firstframe capture rate of the camera to a second frame capture rate based onthe magnitude of difference; wherein determining the direction of travelfor the device further comprises: determining a trajectory of thedevice; and predicting a future direction of travel for the device basedon the trajectory of the device, wherein the future direction of travelis used as the direction of travel for comparing with the firstdirection of capture.
 2. The device of claim 1, wherein execution of theone or more instructions further cause the device to adjust the firstframe rate by a variable amount of adjustment, wherein the variableamount of adjustment is based on the magnitude of difference.
 3. Thedevice of claim 1, wherein execution of the one or more instructionscause the device to further: determine a speed of the device, whereinadjusting the first frame capture rate is further based on the speed ofthe device.
 4. The device of claim 1, wherein execution of the one ormore instructions cause the device to further: determine an accelerationof the device, wherein adjusting the first frame capture rate is furtherbased on the acceleration of the device.
 5. The device of claim 1,wherein execution of the one or more instructions further causes thedevice to perform at least one from the group consisting of: adjusting apower of a RADAR antenna based on the magnitude of difference; adjustinga transmit power of a speaker of a SONAR sensor based on the magnitudeof difference; adjusting a sounding frequency of the SONAR sensor basedon the magnitude of difference; adjusting a power of an emitter for alaser sensor based on the magnitude of difference; adjusting a frequencyof a laser of the laser sensor based on the magnitude of difference;adjusting a movement of the laser of the laser sensor based on themagnitude of difference; adjusting a strobe frequency of the emitter forthe laser sensor based on the magnitude of difference; adjusting a powerof an emitter for a LIDAR sensor based on the magnitude of difference;adjusting a frequency of a laser of the LIDAR sensor based on themagnitude of difference; adjusting a color depth of the camera based onthe magnitude of difference; and adjusting a resolution of the camerabased on the magnitude of difference.
 6. The device of claim 1, whereinexecution of the one or more instructions cause the device to further:determine a density of objects in the device's environment using thecamera, wherein adjusting the first frame capture rate is further basedon the determined density.
 7. The device of claim 1, wherein executionof the one or more instructions cause the device to further: identify anobject as a potential obstacle using one or more captures from thecamera, wherein adjusting the first frame capture rate is further basedon the identification of the object.
 8. The device of claim 7, whereinexecution of the one or more instructions cause the device to further:determine a movement of the identified object, wherein adjusting theframe capture rate is further based on the movement of the identifiedobject.
 9. The device of claim 1, wherein execution of the one or moreinstructions further causes the device to adjust at least one from thegroup consisting of: a frequency of processing information captured bythe camera based on the magnitude of difference; and a portion of theinformation captured by the camera to be processed by the device basedon the magnitude of difference.
 10. The device of claim 1, wherein thedevice is a virtual reality headset.
 11. A method of operating a cameraof a device, wherein the camera includes a first direction of captureand a first frame capture rate, comprising: determining a direction oftravel for the device; comparing the direction of travel with the firstdirection of capture to determine a magnitude of difference; andadjusting the first frame capture rate of the camera to a second framecapture rate based on the magnitude of difference; wherein determiningthe direction of travel for the device comprises: determining atrajectory of the device; and predicting a future direction of travelfor the device based on the trajectory of the device, wherein the futuredirection of travel is used as the direction of travel for comparingwith the first direction of capture.
 12. The method of claim 11, whereinadjusting the first frame capture rate comprises adjusting the firstframe capture rate by a variable amount of adjustment, wherein thevariable amount of adjustment is based on the magnitude of difference.13. The method of claim 11, further comprising at least one from thegroup consisting of: adjusting a power of a RADAR antenna based on themagnitude of difference; adjusting a transmit power of a speaker of aSONAR sensor based on the magnitude of difference; adjusting a soundingfrequency of the SONAR sensor based on the magnitude of difference;adjusting a power of an emitter for a laser sensor based on themagnitude of difference; adjusting a frequency of a laser of the lasersensor based on the magnitude of difference; adjusting a movement of thelaser of the laser sensor based on the magnitude of difference;adjusting a strobe frequency of the emitter for the laser sensor basedon the magnitude of difference; adjusting a power of an emitter for aLIDAR sensor based on the magnitude of difference; adjusting a frequencyof a laser of the LIDAR sensor based on the magnitude of difference;adjusting a color depth of a camera based on the magnitude ofdifference; and adjusting a resolution of the camera based on themagnitude of difference.
 14. The method of claim 11, further comprising:determining a density of objects in the device's environment using thecamera, wherein adjusting the first frame capture rate is further basedon the density.
 15. The method of claim 11, further comprising:identifying an object as a potential obstacle using one or more capturesfrom the camera, wherein adjusting the first frame capture rate isfurther based on the identification of the object.
 16. The method ofclaim 15, further comprising: determining a movement of the identifiedobject, wherein adjusting the first frame capture rate is further basedon the movement of the identified object.
 17. The method of claim 11,further comprising at least one from the group consisting of: adjustinga frequency of processing information captured by the camera based onthe magnitude of difference; and adjusting a portion of the informationcaptured by the camera to be processed by the device based on themagnitude of difference.
 18. A non-transitory computer-readable storagemedium storing one or more programs containing instructions that, whenexecuted by one or more processors of a device, cause the device toperform operations comprising: determining a direction of travel of thedevice, the device having a camera including a first direction ofcapture and a first frame capture rate; comparing the direction oftravel to the first direction of capture to determine a magnitude ofdifference; and adjusting the first frame capture rate of the of thecamera to a second frame capture rate based on the magnitude ofdifference; wherein determining the direction of travel for the devicecauses the device to perform operations further comprising: determininga trajectory of the device; and predicting a future direction of travelfor the device based on the trajectory of the device, wherein the futuredirection of travel is used as the direction of travel for comparing tothe first direction of capture.
 19. The non-transitory computer-readablestorage medium of claim 18, wherein execution of the instructions toadjust the first frame capture rate causes the device to performoperations further comprising adjusting the first frame capture rate bya variable amount of adjustment, wherein the variable amount ofadjustment is based on the magnitude of difference.
 20. Thenon-transitory computer-readable storage medium of claim 18, whereinexecution of the instructions causes the device to perform operationsfurther comprising at least one from the group consisting of:determining a speed of the device, wherein adjusting the first framecapture rate is further based on the speed of the device; anddetermining an acceleration of the device, wherein adjusting the firstframe capture rate is further based on the acceleration of the device.21. The non-transitory computer-readable storage medium of claim 18,wherein execution of the instructions further causes the device toperform operations comprising at least one from the group consisting of:adjusting a power of a RADAR antenna based on the magnitude ofdifference; adjusting a transmit power of a speaker of a SONAR sensorbased on the magnitude of difference; adjusting a sounding frequency ofthe SONAR sensor based on the magnitude of difference; adjusting a powerof an emitter for a laser sensor based on the magnitude of difference;adjusting a frequency of a laser of the laser sensor based on themagnitude of difference; adjusting a movement of the laser of the lasersensor based on the magnitude of difference; adjusting a strobefrequency of the emitter for the laser sensor based on the magnitude ofdifference; adjusting a power of an emitter for a LIDAR sensor based onthe magnitude of difference; adjusting a frequency of a laser of theLIDAR sensor based on the magnitude of difference; adjusting a colordepth of a camera based on the magnitude of difference; and adjusting aresolution of the camera based on the magnitude of difference.
 22. Thenon-transitory computer-readable storage medium of claim 18, whereinexecution of the instructions causes the device to perform operationsfurther comprising: determining a density of objects in the device'senvironment, wherein adjusting the first frame capture rate is furtherbased on the density.
 23. The non-transitory computer-readable storagemedium of claim 18, wherein execution of the instructions causes thedevice to perform operations further comprising: identifying an objectas a potential obstacle using one or more captures from the camera,wherein adjusting the first frame capture rate is further based on theidentification of the object.
 24. The non-transitory computer-readablestorage medium of claim 18, wherein execution of the instructionsfurther causes the device to perform operations including at least onefrom the group consisting of: adjusting a frequency of processinginformation captured by the camera based on the magnitude of difference;and adjusting a portion of the information captured by the camera to beprocessed by the device based on the magnitude of difference.
 25. Adevice including a camera including a first direction of capture and afirst frame capture rate, comprising: means for determining a directionof travel of the device; means for comparing the direction of travel tothe first direction of capture to determine a magnitude of difference;and means for adjusting the first frame capture rate of the camera to asecond frame capture rate based on the magnitude of difference; whereinthe means for determining the direction of travel of the device furtherperforms: determining a trajectory of the device; and predicting afuture direction of travel for the device based on the trajectory of thedevice, wherein the future direction of travel is used as the directionof travel for comparing to the first direction of capture.